



"""simple docstring"""

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
							import torch

							from transformers import AutoModelForImageClassification

if is_vision_available():
							from transformers import AutoImageProcessor
@require_torch
@require_vision
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	@slow
	def 			_lowerCAmelCase     (      self				:       str    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'    )
					model.to(_SCREAMING_SNAKE_CASE    )

					from datasets import load_dataset

					SCREAMING_SNAKE_CASE						:							List[str]							=     load_dataset('nielsr/rvlcdip-demo'    )

					SCREAMING_SNAKE_CASE						:							str							=     dataset['train'][0]['image'].convert('RGB'    )

					SCREAMING_SNAKE_CASE						:							Tuple							=     image_processor(_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    ).to(_SCREAMING_SNAKE_CASE    )

					# forward pass
					with torch.no_grad():
									SCREAMING_SNAKE_CASE						:							List[Any]							=     model(**_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     outputs.logits

					SCREAMING_SNAKE_CASE						:							int							=     torch.Size((1, 16)    )
					self.assertEqual(logits.shape     ,       _SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.tensor(
					    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.float     ,       )
					self.assertTrue(torch.allclose(logits[0, :3]     ,       _SCREAMING_SNAKE_CASE     ,       atol=1E-4    )    )












"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






